
pip install yfinance

Collecting yfinanceNote: you may need to restart the kernel to use updated packages.

  Obtaining dependency information for yfinance from https://files.pythonhosted.org/packages/58/f7/a966b800b49cb5379e348bf0717df481051714a99ece15289718562f6952/yfinance-0.2.35-py2.py3-none-any.whl.metadata
  Downloading yfinance-0.2.35-py2.py3-none-any.whl.metadata (11 kB)
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Collecting multitasking>=0.0.7 (from yfinance)
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Requirement already satisfied: pytz>=2022.5 in c:\users\yasha khanna\.conda\anaconda\lib\site-packages (from yfinance) (2023.3.post1)
Collecting frozendict>=2.3.4 (from yfinance)
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Collecting peewee>=3.16.2 (from yfinance)
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Collecting html5lib>=1.1 (from yfinance)
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Building wheels for collected packages: frozendict, peewee
  Building wheel for frozendict (pyproject.toml): started
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  Stored in directory: c:\users\yasha khanna\appdata\local\pip\cache\wheels\31\dd\81\a814e6f8cde8a1bbc1f088fdc273943371f10478b91a605e14
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  Stored in directory: c:\users\yasha khanna\appdata\local\pip\cache\wheels\02\20\23\74a10d0cd31f5d41c19b92ddf4c138ceff01b9f4675f19dbf5
Successfully built frozendict peewee
Installing collected packages: peewee, multitasking, html5lib, frozendict, yfinance
Successfully installed frozendict-2.4.0 html5lib-1.1 multitasking-0.0.11 peewee-3.17.0 yfinance-0.2.35


import pandas as pd
import yfinance as yf
import datetime
from datetime import date, timedelta
today = date.today()


d1 = today.strftime("%Y-%m-%d")
end_date = d1
d2 = date.today() - timedelta(days=730)
d2 = d2.strftime("%Y-%m-%d")
start_date = d2

data = yf.download('BTC-USD', 
                      start=start_date, 
                      end=end_date, 
                      progress=False)
data["Date"] = data.index
data = data[["Date", "Open", "High", "Low", "Close", "Adj Close", "Volume"]]
data.reset_index(drop=True, inplace=True)
print(data.head())

        Date          Open          High           Low         Close  \
0 2022-01-18  42250.074219  42534.402344  41392.214844  42375.632812   
1 2022-01-19  42374.039062  42478.304688  41242.914062  41744.328125   
2 2022-01-20  41744.027344  43413.023438  40672.824219  40680.417969   
3 2022-01-21  40699.605469  41060.527344  35791.425781  36457.316406   
4 2022-01-22  36471.589844  36688.812500  34349.250000  35030.250000   

      Adj Close       Volume  
0  42375.632812  22417209227  
1  41744.328125  23091543258  
2  40680.417969  20382033940  
3  36457.316406  43011992031  
4  35030.250000  39714385405


data.shape

(730, 7)


import plotly.graph_objects as go
figure = go.Figure(data=[go.Candlestick(x=data["Date"],
                                        open=data["Open"], 
                                        high=data["High"],
                                        low=data["Low"], 
                                        close=data["Close"])])
figure.update_layout(title = "Bitcoin Price Analysis", 
                     xaxis_rangeslider_visible=False)
figure.show()


correlation = data.corr()
print(correlation["Close"].sort_values(ascending=False))

Close        1.000000
Adj Close    1.000000
High         0.997571
Low          0.997499
Open         0.994589
Date         0.045692
Volume      -0.067389
Name: Close, dtype: float64


pip install autots

Collecting autots
  Obtaining dependency information for autots from https://files.pythonhosted.org/packages/84/b9/2a4bd27749d9ff6f91435e4e32707b862769610ff1c0be15e4814676e569/autots-0.6.7-py3-none-any.whl.metadata
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Installing collected packages: autots
Successfully installed autots-0.6.7
Note: you may need to restart the kernel to use updated packages.


from autots import AutoTS
model = AutoTS(forecast_length=30, frequency='infer', ensemble='simple')
model = model.fit(data, date_col='Date', value_col='Close', id_col=None)
prediction = model.predict()
forecast = prediction.forecast
print(forecast)

Data frequency is: D, used frequency is: D
Model Number: 1 with model ARIMA in generation 0 of 10
Model Number: 2 with model AverageValueNaive in generation 0 of 10
Model Number: 3 with model AverageValueNaive in generation 0 of 10
Model Number: 4 with model AverageValueNaive in generation 0 of 10
Model Number: 5 with model DatepartRegression in generation 0 of 10
Model Number: 6 with model DatepartRegression in generation 0 of 10

C:\Users\YASHA KHANNA\.conda\anaconda\Lib\site-packages\sklearn\svm\_classes.py:32: FutureWarning:

The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.

C:\Users\YASHA KHANNA\.conda\anaconda\Lib\site-packages\sklearn\svm\_base.py:1250: ConvergenceWarning:

Liblinear failed to converge, increase the number of iterations.

Model Number: 7 with model DatepartRegression in generation 0 of 10

C:\Users\YASHA KHANNA\.conda\anaconda\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:546: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html

Model Number: 8 with model DatepartRegression in generation 0 of 10
WARNING:tensorflow:From C:\Users\YASHA KHANNA\.conda\anaconda\Lib\site-packages\keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.

WARNING:tensorflow:From C:\Users\YASHA KHANNA\.conda\anaconda\Lib\site-packages\keras\src\backend.py:277: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.

WARNING:tensorflow:From C:\Users\YASHA KHANNA\.conda\anaconda\Lib\site-packages\keras\src\optimizers\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.

Epoch 1/50
WARNING:tensorflow:From C:\Users\YASHA KHANNA\.conda\anaconda\Lib\site-packages\keras\src\utils\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.

22/22 [==============================] - 13s 10ms/step - loss: 0.4158
Epoch 2/50
22/22 [==============================] - 0s 10ms/step - loss: 0.4053
Epoch 3/50
22/22 [==============================] - 0s 10ms/step - loss: 0.3955
Epoch 4/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3789
Epoch 5/50
22/22 [==============================] - 0s 10ms/step - loss: 0.3738
Epoch 6/50
22/22 [==============================] - 0s 10ms/step - loss: 0.3681
Epoch 7/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3655
Epoch 8/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3619
Epoch 9/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3609
Epoch 10/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3602
Epoch 11/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3579
Epoch 12/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3535
Epoch 13/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3529
Epoch 14/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3535
Epoch 15/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3515
Epoch 16/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3519
Epoch 17/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3546
Epoch 18/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3502
Epoch 19/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3491
Epoch 20/50
22/22 [==============================] - 0s 8ms/step - loss: 0.3474
Epoch 21/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3483
Epoch 22/50
22/22 [==============================] - 0s 10ms/step - loss: 0.3451
Epoch 23/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3474
Epoch 24/50
22/22 [==============================] - 0s 10ms/step - loss: 0.3469
Epoch 25/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3396
Epoch 26/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3451
Epoch 27/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3516
Epoch 28/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3430
Epoch 29/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3416
Epoch 30/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3433
Epoch 31/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3422
Epoch 32/50
22/22 [==============================] - 0s 10ms/step - loss: 0.3415
Epoch 33/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3414
Epoch 34/50
22/22 [==============================] - 0s 10ms/step - loss: 0.3379
Epoch 35/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3367
Epoch 36/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3373
Epoch 37/50
22/22 [==============================] - 0s 10ms/step - loss: 0.3401
Epoch 38/50
22/22 [==============================] - 0s 10ms/step - loss: 0.3382
Epoch 39/50
22/22 [==============================] - 0s 10ms/step - loss: 0.3333
Epoch 40/50
22/22 [==============================] - 0s 10ms/step - loss: 0.3409
Epoch 41/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3321
Epoch 42/50
22/22 [==============================] - 0s 10ms/step - loss: 0.3337
Epoch 43/50
22/22 [==============================] - 0s 10ms/step - loss: 0.3351
Epoch 44/50
22/22 [==============================] - 0s 10ms/step - loss: 0.3329
Epoch 45/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3310
Epoch 46/50
22/22 [==============================] - 0s 9ms/step - loss: 0.3351
Epoch 47/50
22/22 [==============================] - 0s 10ms/step - loss: 0.3306
Epoch 48/50
22/22 [==============================] - 0s 10ms/step - loss: 0.3312
Epoch 49/50
22/22 [==============================] - 0s 10ms/step - loss: 0.3242
Epoch 50/50
22/22 [==============================] - 0s 10ms/step - loss: 0.3294
1/1 [==============================] - 2s 2s/step
Model Number: 9 with model ETS in generation 0 of 10
Model Number: 10 with model ETS in generation 0 of 10
Model Number: 11 with model GLM in generation 0 of 10
Model Number: 12 with model GLM in generation 0 of 10
Model Number: 13 with model GLS in generation 0 of 10
Model Number: 14 with model GLS in generation 0 of 10
Model Number: 15 with model GluonTS in generation 0 of 10
Template Eval Error: ImportError('GluonTS installation is incompatible with AutoTS. The numpy version is sometimes the issue, try 1.23.1 {as of 06-2023}') in model 15 in generation 0: GluonTS
Model Number: 16 with model GluonTS in generation 0 of 10
Template Eval Error: ImportError('GluonTS installation is incompatible with AutoTS. The numpy version is sometimes the issue, try 1.23.1 {as of 06-2023}') in model 16 in generation 0: GluonTS
Model Number: 17 with model GluonTS in generation 0 of 10
Template Eval Error: ImportError('GluonTS installation is incompatible with AutoTS. The numpy version is sometimes the issue, try 1.23.1 {as of 06-2023}') in model 17 in generation 0: GluonTS
Model Number: 18 with model GluonTS in generation 0 of 10
Template Eval Error: ImportError('GluonTS installation is incompatible with AutoTS. The numpy version is sometimes the issue, try 1.23.1 {as of 06-2023}') in model 18 in generation 0: GluonTS
Model Number: 19 with model GluonTS in generation 0 of 10
Template Eval Error: ImportError('GluonTS installation is incompatible with AutoTS. The numpy version is sometimes the issue, try 1.23.1 {as of 06-2023}') in model 19 in generation 0: GluonTS
Model Number: 20 with model LastValueNaive in generation 0 of 10
Model Number: 21 with model LastValueNaive in generation 0 of 10
Model Number: 22 with model LastValueNaive in generation 0 of 10
Model Number: 23 with model LastValueNaive in generation 0 of 10
Model Number: 24 with model SeasonalNaive in generation 0 of 10
Model Number: 25 with model SeasonalNaive in generation 0 of 10
Model Number: 26 with model SeasonalNaive in generation 0 of 10
Model Number: 27 with model UnobservedComponents in generation 0 of 10
Model Number: 28 with model UnobservedComponents in generation 0 of 10
Model Number: 29 with model UnobservedComponents in generation 0 of 10
Model Number: 30 with model VAR in generation 0 of 10
Template Eval Error: ValueError('Only gave one variable to VAR') in model 30 in generation 0: VAR
Model Number: 31 with model VAR in generation 0 of 10
Template Eval Error: ValueError('Only gave one variable to VAR') in model 31 in generation 0: VAR
Model Number: 32 with model VECM in generation 0 of 10
Template Eval Error: ValueError('Only gave one variable to VECM') in model 32 in generation 0: VECM
Model Number: 33 with model VECM in generation 0 of 10
Template Eval Error: ValueError('Only gave one variable to VECM') in model 33 in generation 0: VECM
Model Number: 34 with model WindowRegression in generation 0 of 10
Model Number: 35 with model ConstantNaive in generation 0 of 10

C:\Users\YASHA KHANNA\.conda\anaconda\Lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:546: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html

Model Number: 36 with model FBProphet in generation 0 of 10
Template Eval Error: ModuleNotFoundError("No module named 'fbprophet'") in model 36 in generation 0: FBProphet
Model Number: 37 with model GluonTS in generation 0 of 10
Template Eval Error: ImportError('GluonTS installation is incompatible with AutoTS. The numpy version is sometimes the issue, try 1.23.1 {as of 06-2023}') in model 37 in generation 0: GluonTS
Model Number: 38 with model MultivariateRegression in generation 0 of 10
Model Number: 39 with model MultivariateRegression in generation 0 of 10
Template Eval Error: ValueError("regression_type='User' but not future_regressor supplied.") in model 39 in generation 0: MultivariateRegression
Model Number: 40 with model DatepartRegression in generation 0 of 10
Template Eval Error: ValueError("regression_type='User' but no future_regressor passed") in model 40 in generation 0: DatepartRegression
Model Number: 41 with model SeasonalNaive in generation 0 of 10
Model Number: 42 with model DatepartRegression in generation 0 of 10
Model Number: 43 with model UnobservedComponents in generation 0 of 10
Model Number: 44 with model UnobservedComponents in generation 0 of 10
Model Number: 45 with model ETS in generation 0 of 10
Model Number: 46 with model VECM in generation 0 of 10
Template Eval Error: ValueError('Only gave one variable to VECM') in model 46 in generation 0: VECM
Model Number: 47 with model ARDL in generation 0 of 10
Model Number: 48 with model MultivariateMotif in generation 0 of 10
Model Number: 49 with model MultivariateMotif in generation 0 of 10
Model Number: 50 with model UnivariateMotif in generation 0 of 10
Model Number: 51 with model UnivariateMotif in generation 0 of 10
Model Number: 52 with model SectionalMotif in generation 0 of 10
Model Number: 53 with model SectionalMotif in generation 0 of 10
Model Number: 54 with model MultivariateRegression in generation 0 of 10
Model Number: 55 with model FBProphet in generation 0 of 10
Template Eval Error: ModuleNotFoundError("No module named 'fbprophet'") in model 55 in generation 0: FBProphet
Model Number: 56 with model SeasonalNaive in generation 0 of 10
Model Number: 57 with model DatepartRegression in generation 0 of 10
Model Number: 58 with model NVAR in generation 0 of 10
Model Number: 59 with model Theta in generation 0 of 10
Model Number: 60 with model UnivariateRegression in generation 0 of 10
Model Number: 61 with model ARCH in generation 0 of 10
Template Eval Error: ImportError('`arch` package must be installed from pip') in model 61 in generation 0: ARCH
Model Number: 62 with model ConstantNaive in generation 0 of 10
Model Number: 63 with model LastValueNaive in generation 0 of 10
Model Number: 64 with model AverageValueNaive in generation 0 of 10
Model Number: 65 with model GLS in generation 0 of 10
Model Number: 66 with model SeasonalNaive in generation 0 of 10
Model Number: 67 with model GLM in generation 0 of 10
Model Number: 68 with model ETS in generation 0 of 10

C:\Users\YASHA KHANNA\.conda\anaconda\Lib\site-packages\statsmodels\genmod\families\links.py:198: RuntimeWarning:

overflow encountered in exp

Model Number: 69 with model FBProphet in generation 0 of 10
Template Eval Error: ModuleNotFoundError("No module named 'fbprophet'") in model 69 in generation 0: FBProphet
Model Number: 70 with model GluonTS in generation 0 of 10
Template Eval Error: ImportError('GluonTS installation is incompatible with AutoTS. The numpy version is sometimes the issue, try 1.23.1 {as of 06-2023}') in model 70 in generation 0: GluonTS
Model Number: 71 with model UnobservedComponents in generation 0 of 10
Model Number: 72 with model VAR in generation 0 of 10
Template Eval Error: ValueError('Only gave one variable to VAR') in model 72 in generation 0: VAR
Model Number: 73 with model VECM in generation 0 of 10
Template Eval Error: LinAlgError('Singular matrix') in model 73 in generation 0: VECM
Model Number: 74 with model ARIMA in generation 0 of 10
Model Number: 75 with model WindowRegression in generation 0 of 10
Model Number: 76 with model DatepartRegression in generation 0 of 10
Template Eval Error: ValueError("regression_type='User' but no future_regressor passed") in model 76 in generation 0: DatepartRegression
Model Number: 77 with model UnivariateRegression in generation 0 of 10
Model Number: 78 with model MultivariateRegression in generation 0 of 10
Model Number: 79 with model UnivariateMotif in generation 0 of 10
Model Number: 80 with model MultivariateMotif in generation 0 of 10
Model Number: 81 with model SectionalMotif in generation 0 of 10
Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 81 in generation 0: SectionalMotif
Model Number: 82 with model NVAR in generation 0 of 10
Model Number: 83 with model Theta in generation 0 of 10
Model Number: 84 with model ARDL in generation 0 of 10
Model Number: 85 with model ARCH in generation 0 of 10
Template Eval Error: ImportError('`arch` package must be installed from pip') in model 85 in generation 0: ARCH
Model Number: 86 with model MetricMotif in generation 0 of 10
Model Number: 87 with model GluonTS in generation 0 of 10
Template Eval Error: ImportError('GluonTS installation is incompatible with AutoTS. The numpy version is sometimes the issue, try 1.23.1 {as of 06-2023}') in model 87 in generation 0: GluonTS
Model Number: 88 with model UnobservedComponents in generation 0 of 10
Model Number: 89 with model SeasonalNaive in generation 0 of 10
Model Number: 90 with model SeasonalNaive in generation 0 of 10
Model Number: 91 with model AverageValueNaive in generation 0 of 10
Model Number: 92 with model SectionalMotif in generation 0 of 10
Template Eval Error: ValueError('Unknown Distance Metric: kulsinski') in model 92 in generation 0: SectionalMotif
Model Number: 93 with model ARIMA in generation 0 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 93 in generation 0: ARIMA
Model Number: 94 with model Theta in generation 0 of 10
Model Number: 95 with model ARDL in generation 0 of 10
Model Number: 96 with model VAR in generation 0 of 10
Template Eval Error: ValueError("x contains one or more constant columns. Column(s) 1 are constant. Adding a constant with trend='c' is not allowed.") in model 96 in generation 0: VAR
Model Number: 97 with model UnivariateMotif in generation 0 of 10
Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 97 in generation 0: UnivariateMotif
Model Number: 98 with model MultivariateMotif in generation 0 of 10
Model Number: 99 with model SeasonalNaive in generation 0 of 10
Model Number: 100 with model GluonTS in generation 0 of 10
Template Eval Error: ImportError('GluonTS installation is incompatible with AutoTS. The numpy version is sometimes the issue, try 1.23.1 {as of 06-2023}') in model 100 in generation 0: GluonTS
Model Number: 101 with model ConstantNaive in generation 0 of 10
Model Number: 102 with model SeasonalNaive in generation 0 of 10
Model Number: 103 with model AverageValueNaive in generation 0 of 10
Model Number: 104 with model VECM in generation 0 of 10
Template Eval Error: ValueError('Only gave one variable to VECM') in model 104 in generation 0: VECM
Model Number: 105 with model ETS in generation 0 of 10
Model Number: 106 with model UnobservedComponents in generation 0 of 10
Model Number: 107 with model LastValueNaive in generation 0 of 10

C:\Users\YASHA KHANNA\.conda\anaconda\Lib\site-packages\sklearn\linear_model\_coordinate_descent.py:628: ConvergenceWarning:

Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.124e+08, tolerance: 3.238e+05